Abstract
This paper presents a novel implementation of a patient fall detection system that may be used for patient activity recognition and emergency treatment. Sensors equipped with accelerometers are attached on the body of the patients and transmit patient movement data wirelessly to the monitoring unit. The methodology of support Vector Machines is used for precise classification of the acquired data and determination of a fall emergency event. Then a context-aware server transmits video from patient site properly coded according to both patient and network status. Evaluation results indicate the high accuracy of the classification method and the effectiveness of the proposed implementation.
Chapter PDF
Similar content being viewed by others
References
Noury N., Herve T., Rialle V., Virone G., Mercier E., Morey G., Moro A., and Porcheron T., Monitoring behavior in home using a smart fall sensor and position sensors, Annual International Conference on Microtechnologies in Medicine and Biology, 607–610 (2000).
Noury N., A smart sensor for the remote follow up of activity and fall detection of the elderly, 2nd Annual International Conference on Microtechnologies in Medicine and Biology, 314–317 (2002).
Prado M., Reina-Tosina J., and Roa L., Distributed intelligent architecture for falling detection and physical activity analysis in the elderly, 24th Annual IEEE EMBS Conference, 1910–1911 (2002).
Fukaya K., Fall detection sensor for fall protection airbag, 41st SICE Annual Conference, 419–420 (2002).
Sixsmith A., and Johnson N., A smart sensor to detect the falls of the elderly, IEEE Pervasing Computing, 3(2), 42–47 (2004).
Nait-Charif, H. and McKenna, S.J., Activity summarisation and fall detection in a supportive home environment, 17th International Conference on Pattern Recognition ICPR 2004, 323–236 (2004).
Hwang, J.Y. Kang, J.M. Jang, Y.W. and Kim, H.C., Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly, 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2204–2207 (2004).
Suhuai Luo, and Qingmao Hu, A dynamic motion pattern analysis approach to fall detection, 2004 IEEE International Workshop on Biomedical Circuits and Systems, 1–8a (2004).
Shuangquan Wang, Jie Yang, Ningjiang Chen, Xin Chen, and Qinfeng Zhang, Human activity recognition with user-free accelerometers in the sensor networks, International Conference on Neural Networks and Brain, 1212–1217 (2005).
S.-G. Miaou, Pei-Hsu Sung, and Chia-Yuan Huang, A Customized Human Fall Detection System Using Omni-Camera Images and Personal Information, 1st Transdisciplinary conference on Distributed Diagnosis and Home Healthcare, 39–42 (2006).
Alwan, M. Rajendran, P.J. Kell, S. Mack, D. Dalai, S. Wolfe, M. and Felder, R. A Smart and Passive Floor-Vibration Based Fall Detector for Elderly, 2nd Information and Communication Technologies Conference, ICTTA 06, 1003–1007 (2006).
Allen, F.R. Ambikairajah, E. Lovell N.H. and Celler, B.G., An Adapted Gaussian Mixture Model Approach to Accelerometry-Based Movement Classification Using Time-Domain Features, 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3600–3603 (2006).
Rougier, C. Meunier, J. St-Arnaud, A. and Rousseau, J., Monocular 3D Head Tracking to Detect Falls of Elderly People, 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 6384–6387, (2006).
Cao, X.B. Chen, D. Qiao, H. and Xu, Y.W., An Evolutionary Support Vector Machines Classifier for Pedestrian Detection, 2006 IEEE International Conference on Intelligent Robots and Systems, 4223–4227 (2006).
Jansen Bart, and Deklerck Rudi, Context aware inactivity recognition for visual fall detection, 2006 Pervasive Health Conference and Workshops, 1–4 (2006).
Gaura, E.I., Rider, R.J., Steele, N., and Naguib R.N.G., Neural-network compensation methods for capacitive micromachined accelerometers for use in telecare medicine, IEEE Transactions on Information Technology in Biomedicine, 5,(3), 248–252 (2001).
The IEEE 802.15.4 ZigBee wireless technology; http://www.zigbee.org
The SARD sensor node; http://www.freescale.com
Christianini N, and Shawe-Taylor J, An introduction to support vector machines. (Cambridge University Press, 2000).
Schölkopf B: Statistical learning and kernel methods. [http://research.Microsoft.com/~bsc].
Chih-Chung Chang and Chih-Jen Lin, LIBSVM: A library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
The ITU H.263 compression standard, available in http://www.itu.int/itudoc/itu-t/rec/h/
Sasiadek J.Z., and Khe J., Sensor fusion based on fuzzy Kalman filter, 2nd International IEEE Workshop on Robot Motion and Control, 275–283, (2001).
Mohinder S. Grewal, Angus P. Andrews, Kalman Filtering (Second Edition), (John Wiley & Sons, 2001).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 International Federation for Information Processing
About this paper
Cite this paper
Doukas, C., Maglogiannis, I., Tragas, P., Liapis, D., Yovanof, G. (2007). Patient Fall Detection using Support Vector Machines. In: Boukis, C., Pnevmatikakis, A., Polymenakos, L. (eds) Artificial Intelligence and Innovations 2007: from Theory to Applications. AIAI 2007. IFIP The International Federation for Information Processing, vol 247. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74161-1_16
Download citation
DOI: https://doi.org/10.1007/978-0-387-74161-1_16
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-74160-4
Online ISBN: 978-0-387-74161-1
eBook Packages: Computer ScienceComputer Science (R0)